Computer Science undergraduate deeply passionate about building and shipping AI systems — not just training models, but taking them all the way from raw data to deployed, production-ready applications.
I focus on end-to-end AI pipelines: dataset curation, model architecture, training, evaluation, optimization, and deployment behind clean REST APIs. Every project I build is designed to work in the real world — fast, accurate, and maintainable.
"I don't just train models. I build systems around them."
- Python (Advanced — ML, DL, scripting, APIs)
- Java (OOP & DSA foundations)
- SQL (Querying, schema design, optimization)
- Exploratory Data Analysis (EDA) & Feature Engineering
- Supervised Learning — Classification, Regression, Ensemble Methods
- Model Evaluation (AUC, F1, Precision, Recall, Confusion Matrix)
- NLP — Text preprocessing, Sentiment Analysis, Summarization
- Information Retrieval Systems (TF-IDF, Inverted Index, Boolean Retrieval)
- Kaggle competitions & real-world datasets
- TensorFlow & Keras — model design, training loops, callbacks, fine-tuning
- PyTorch — custom architectures, research experiments, flexible pipelines
- Transfer Learning & Fine-tuning (EfficientNet, ResNet, MobileNet, VGG)
- YOLO — object detection, custom dataset training, real-time inference
- Image Classification — CNNs, data augmentation, class imbalance handling
- Medical Imaging — preprocessing, clinical metric optimization
- OpenCV — real-time vision pipelines, frame processing, camera integration
- FastAPI — high-performance REST APIs for ML model serving
- Flask — lightweight API backends and web interfaces
- Docker — containerizing AI applications for reproducible deployment
- GitHub Actions — CI/CD pipelines, automated testing and deployment
- Streamlit — rapid ML demo and data app interfaces
- Clean API architecture — route design, request validation, error handling
- PostgreSQL, MySQL, SQLite, MongoDB
- SQL querying, schema design, indexing
- Linux (CLI, environment management, scripting)
- Git & GitHub (version control, clean repo practices)
- Jupyter Notebooks & Google Colab (training & experimentation)
- Kaggle (competition datasets, GPU-accelerated training)
End-to-end AI system helping blind and visually impaired users navigate their environment independently.
- Real-time YOLO-based object detection for obstacle identification and environment understanding
- Custom-trained model optimized for low-latency mobile inference
- Voice-guided feedback system with spoken environment descriptions
- FastAPI backend handling model inference, request routing, and response delivery
- React Native (Expo) mobile client consuming the AI backend
- Focused on real-world usability, accessibility, and inclusive design
End-to-end deep learning pipeline for medical image classification — built with patient safety as the primary constraint.
- Transfer learning with EfficientNet, fine-tuned on breast ultrasound imaging data
- Full training pipeline: preprocessing, augmentation, class imbalance handling, training, evaluation
- Clinically relevant evaluation: AUC, Precision, Recall, F1 — optimized to minimize false negatives
- Deployed as a production-ready web application via FastAPI with image upload interface
- Demonstrates full AI systems lifecycle: data → model → API → application
Real-time computer vision system for ID card localization and institutional verification.
- Custom YOLO model trained on a purpose-built dataset for ID card detection
- Real-time camera feed processing for on-the-spot verification
- FastAPI backend for inference requests with fast response times
- Full end-to-end CV pipeline: data collection → annotation → training → deployment
A comprehensive collection of ML & DL implementations built throughout coursework and independent research.
- Covers: Classification, Regression, CNNs, NLP, Object Detection
- Includes: Malaria Cell Classification, House Price Predictor, and more
- Each project includes full training code, evaluation, and FastAPI deployment
- Well-documented notebooks suitable for learning and reference
- 📂 View Repository
- 📌 AI Systems Developer / ML Engineer
- 📌 Computer Vision & Medical AI
- 📌 Model Training, Fine-tuning & Optimization
- 📌 MLOps & Production Model Deployment
- 📌 End-to-End AI Pipeline Engineering